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Electrocardiography

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Investigating the correlation between smoking and blood pressure via photoplethysmography.

Biomedical engineering online
Smoking has been widely identified for its detrimental effects on human health, particularly on the cardiovascular health. The prediction of these effects can be anticipated by monitoring the dynamic changes in vital signs and other physiological sig...

Machine learning to risk stratify chest pain patients with non-diagnostic electrocardiogram in an Asian emergency department.

Annals of the Academy of Medicine, Singapore
INTRODUCTION: Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers a...

Research on noninvasive electrophysiologic imaging based on cardiac electrophysiology simulation and deep learning methods for the inverse problem.

BMC cardiovascular disorders
BACKGROUND: The risk stratification and prognosis of cardiac arrhythmia depend on the individual condition of patients, while invasive diagnostic methods may be risky to patient health, and current non-invasive diagnostic methods are applicable to fe...

Heart failure monitoring with a single‑lead electrocardiogram at home.

International journal of cardiology
BACKGROUND: Repeated hospitalization due to heart failure (HF) is a significant predictor of mortality. However, there are limited early detection systems for HF progression that can be utilized by patients at home without a cardiac implantable elect...

Prediction of significant congenital heart disease in infants and children using continuous wavelet transform and deep convolutional neural network with 12-lead electrocardiogram.

BMC pediatrics
BACKGROUND: Congenital heart disease (CHD) affects approximately 1% of newborns and is a leading cause of mortality in early childhood. Despite the importance of early detection, current screening methods, such as pulse oximetry and auscultation, hav...

Classification of multi-lead ECG based on multiple scales and hierarchical feature convolutional neural networks.

Scientific reports
Detecting and classifying arrhythmias is essential in diagnosing cardiovascular diseases. However, current deep learning-based classification methods often encounter difficulties in effectively integrating both the morphological and temporal features...

Dynamic cross-domain transfer learning for driver fatigue monitoring: multi-modal sensor fusion with adaptive real-time personalizations.

Scientific reports
Driver fatigue is one of the most common causes of road accidents, which means that there is a great need for robust and adaptive monitoring systems. Current models of fatigue detection suffer from domain-specific limitations in generalizing across d...

Optimized deep residual networks for early detection of myocardial infarction from ECG signals.

BMC cardiovascular disorders
Globally, the high number of deaths are happening due to Myocardial infarction (MI). MI is considered as a life-threatening disease, which leads to an increase number of deaths or damage to the heart, and hence, prompt detection of MI is critical to ...

IoT driven smart health monitoring for heart disease prediction using quantum kernel enhanced sardine diffusion and CNN.

Scientific reports
Heart disease is one of the major causes of death worldwide, and the traditional diagnostic procedures typically cause delays in treatment, particularly in low-resource regions. In this article, we propose a novel IoT-based Quantum Kernel-Enhanced Sa...

Transfer learning in ECG diagnosis: Is it effective?

PloS one
The adoption of deep learning in ECG diagnosis is often hindered by the scarcity of large, well-labeled datasets in real-world scenarios, leading to the use of transfer learning to leverage features learned from larger datasets. Yet the prevailing as...